Bericht
Nowcasting the Finnish economy with a large Bayesian vector autoregressive model
Timely and accurate assessment of current macroeconomic activity is crucial for policymakers and other economic agents. Nowcasting aims to forecast the current economic situation ahead of official data releases. We develop and apply a large Bayesian vector autoregressive (BVAR) model to nowcast quarterly GDP growth rate of the Finnish economy. We study the BVAR model’s out-of-sample performance at different forecasting horizons, and compare to various bridge models and a dynamic factor model.
- Language
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Englisch
- Bibliographic citation
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Series: BoF Economics Review ; No. 6/2017
- Classification
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Wirtschaft
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Subject
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ennusteet
mallit
BVAR
Suomi
bkt
- Event
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Geistige Schöpfung
- (who)
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Itkonen, Juha
Juvonen, Petteri
- Event
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Veröffentlichung
- (who)
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Bank of Finland
- (where)
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Helsinki
- (when)
-
2017
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Bericht
Associated
- Itkonen, Juha
- Juvonen, Petteri
- Bank of Finland
Time of origin
- 2017
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